Adaptive ankle impedance control for bipedal robotic upright balance

Upright balance control is a fundamental skill of bipedal robots for various tasks that are usually performed by human beings. Conventional robotic control is often realized by developing accurate dynamic models using a series of fixed torque-ankle states, but their success is subject to accurate physical and kinematic models. This can be particularly challenging when external disturbing forces present, but this is common in unstructured robotic working environments, leading to ineffective robotic control. To address such limitation, this paper presents an adaptive ankle impedance control method with the support of the advances of adaptive fuzzy inference systems, by which the desired ankle torques are generated in real time to adaptively meet the dynamic control requirement. In particular, the control method is initialised with specific external disturbing forces first representing a general situation, which then evolves whilst performing in a real-world working environment by acting on the feedback from the control system. This is implemented by initialising a rule base for a typical situation, and then allowing the rule base to evolve to specific robotic working environments. This closed loop feedback and action mechanism timely and effectively configures the control system to meet the dynamic control requirements. The proposed control method was applied to a bipedal robot on a moving vehicle for system validation and evaluation, with robotic loads ranging from 0 to 1.65 kg and external disturbances in terms of vehicle acceleration ranging from 0.5 to 1.5 m/s, leading to robotic swing angles up to 7.6º and anti-disturbance timespans up to 8.5 s. These experimental results demonstrate the power of the proposed upright balance control method in improving the robustness, and thus applicability, of bipedal robots

Ankle Variable Impedance Control for Humanoid Robot Upright Balance Control

Upright balance control is the most fundamental, yet essential, function of a humanoid robot to enable the performance of various tasks that are traditionally performed by human being in various unstructured environments. Such control schemes were conventionally implemented by developing accurate physical and kinematic models based on fixed torque-ankle states, which often lack robustness to external disturbing forces. This paper presents a variable impedance control method that generates the desired torques for stable humanoid robot upright balance control, to address this limitation. The robustness of the proposed method was brought by a variable parameter approach with the support of the impedance model. The variable parameter of the ankle angle is able to describe the balance state of a humanoid robot, and the proper adjustment of such parameter ensures the effectiveness of the control model. The proposed approach was applied to a humanoid robot on a moving vehicle, and the experimental results demonstrated its efficacy and robustness

Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph

The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation

3D Localization Method of Partial Discharge in Air-Insulated Substation Based on Improved Particle Swarm Optimization Algorithm

Partial discharge (PD) localization in an air-insulated substation (AIS) can be used to assess insulation conditions efficiently. Many localization methods have been reported during the past few years. However, the error of the localization results has been large or the localization algorithm too inefficient. The reason is that the localization equation set is nonlinear and non-symmetrical. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed to improve the localization accuracy in 3D. Firstly, the proposed localization method is based on the symmetrical antenna array and the location error distribution is analyzed. Secondly, the objective function of PSO is constructed using the error distribution. Specifically, the 3D location target is divided into two steps—plane coordinates and height. The two targets are optimized respectively. To verify the method, a test is carried out by a prefabricated fault bushing in the laboratory to compare with the existing methods. According to the results, the localization error is 0.21 m, which can locate the PD source accurately. A complete calculation takes 42.29 s, and the efficiency is increased by 16.13 times under the same accuracy. The comparison results show that the proposed method can greatly improve the efficiency while ensuring accuracy

Using Adaptive Directed Acyclic Graph for Human In-Hand Motion Identification with Hybrid Surface Electromyography and Kinect

The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the humans by observing the state information between an object and the human hand, then transferring the manipulation skills into bionic multi-fingered robotic hand realizing dexterous in-hand manipulation. First, an Adaptive Directed Acyclic Graph (ADAG) algorithm for recognizing HIMs is proposed and optimized based on the comparison of multi-class support vector machines; second, ten representative complex in-hand motions are demonstrated by ten subjects, and SEMG and Kinect signals are obtained based on a multi-modal data acquisition platform; then, combined with the proposed algorithm framework, a series of data preprocessing algorithms are realized. There is statistical symmetry in similar types of SEMG signals and images, and asymmetry in different types of SEMG signals and images. A detailed analysis and an in-depth discussion are given from the results of the ADAG recognizing HIMs, motion recognition rates of different perceptrons, motion recognition rates of different subjects, motion recognition rates of different multi-class SVM methods, and motion recognition rates of different machine learning methods. The results of this experiment confirm the feasibility of the proposed method, with a recognition rate of 95.10%

GIS Partial Discharge Pattern Recognition Based on Multi-Feature Information Fusion of PRPD Image

Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD based on multi-feature information fusion is proposed in this paper. Firstly, a recognition model based on quasi-Hausdorff distance is established according to the statistical characteristics of the phase-resolved partial discharge (PRPD) image, and then a modified convolutional neural network recognition model is established according to the image features of the PRPD image. Finally, Dempster–Shafer (D–S) evidence theory is used to fuse the two pattern recognition results and complement the advantages of the two approaches to improve the accuracy of partial discharge pattern recognition. The experimental results show that the total recognition accuracy rate of this method for four typical PD is more than 94.00%, and the recognition rate is significantly improved compared to support vector machine and normal convolution neural network. Maintaining stability in typical bipedal robots is challenging due to two main reasons